Heart Disease, Clinical Proteomics and Mass Spectrometry
Bibliographic record
Abstract
Heart disease is the leading cause of mortality and morbidity in the world. As such, biomarkers are needed for the diagnosis, prognosis, therapeutic monitoring and risk stratification of acute injury (acute myocardial infarction (AMI)) and chronic disease (heart failure). The procedure for biomarker development involves the discovery, validation, and translation into clinical practice of a panel of candidate proteins to monitor risk of heart disease. Two types of biomarkers are possible; heart-specific and cardiovascular pulmonary system monitoring markers. Here we review the use of MS in the process of cardiac biomarker discovery and validation by proteomic analysis of cardiac myocytes/tissue or serum/plasma. An example of the use of MS in biomarker discovery is given in which the albumin binding protein sub-proteome was examined using MALDI-TOF MS/MS. Additionally, an example of MS in protein validation is given using affinity surface enhanced laser desorption ionization (SELDI) to monitor the disease-induced post-translational modification and the ternary status of myocyte-originating protein, cardiac troponin I in serum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".